Accurate traffic prediction is important for efficient traffic operation, management, and user convenience. It enables traffic management authorities to allocate traffic resources efficiently, reducing traffic congestion and minimizing travel time for commuters. With the increase in data sources, traffic prediction methods have shifted from traditional model-based approaches to more data-driven methods. However, accurately predicting traffic under unforeseen events, such as crashes, adverse weather conditions, and road works, remains a challenging task. Hybrid traffic prediction models that combine data-driven and model-based approaches have emerged as promising solutions, considering the advantage of the model-based approach that can replicate unobserved scenarios. This paper proposes a hybrid traffic prediction framework named SMURP (Simulation and Machine-learning Utilization for Real-time Prediction), which overcomes the limitations of the existing methods. The SMURP is a framework that can be applied to any data-driven prediction method. When an event is detected during prediction, the SMURP complements the prediction outcomes with an additional predictor that uses simulated traffic data. The proposed framework is applied to various data-driven prediction models and evaluated in the actual road section. The results show that applying the SMURP to data-driven prediction methods can improve prediction accuracy.

准确的交通预测对于高效的交通运营、管理和方便用户具有重要意义。它使交通管理机构能够有效地分配交通资源，减少交通拥堵并最大限度地缩短通勤者的出行时间。随着数据源的增加，交通预测方法已经从传统的基于模型的方法转向更多数据驱动的方法。然而，准确预测不可预见事件（例如碰撞、恶劣天气条件和道路施工）下的交通仍然是一项具有挑战性的任务。考虑到基于模型的方法可以复制未观察到的场景的优势，结合了数据驱动和基于模型的方法的混合交通预测模型已成为有前景的解决方案。本文提出了一种名为 SMURP（Simulation and Machine-learning Utilization for Real-time Prediction）的混合流量预测框架，克服了现有方法的局限性。SMURP 是一个可应用于任何数据驱动预测方法的框架。当在预测过程中检测到事件时，SMURP 会使用使用模拟交通数据的附加预测器来补充预测结果。所提出的框架适用于各种数据驱动的预测模型，并在实际路段中进行评估。结果表明，将SMURP应用于数据驱动的预测方法可以提高预测精度。